The present invention generally relates to the field of image reconstruction in computed tomography (CT) systems, and more particularly to a system and method for detecting contraband through the use of variance data.
CT systems operate by projecting fan-shaped, cone-shaped or helically shaped X-ray beams through an object. The X-ray beams are generated by an X-ray source, and are generally collimated prior to passing through the object being scanned. The attenuated beams are then detected by a set of detector elements. The detector elements produce a signal based on the intensity of the attenuated X-ray beams, and the signals are processed to produce projections. By using reconstruction techniques such as filtered backprojection, useful images are formed from these projections.
A computer is able to process and reconstruct images of the portions of the object responsible for the radiation attenuation. As will be appreciated by those skilled in the art, these images are computed by processing a series of angularly displaced and possibly translated projection images. This data is then reconstructed to produce the image, which is typically displayed on a cathode ray tube, and may be printed or reproduced on film.
Traditional CT reconstruction techniques comprise reconstructing the mean CT number at each voxel. However, there is variability in that value caused by noise factors such as photon noise (X-ray noise), quantization noise and electronic noise in the projection measurements interacting with the reconstruction process, and by other physical effects and sources of artifacts. It is therefore advantageous not only to reconstruct the mean CT number, but also the variance associated with each voxel for improved image analysis. In addition, a point wise variance estimate for each voxel also provides additional diagnostic information about the reconstructed image.
One way of generating a variance image is to take an ensemble of images, reconstruct each image, and then compute the variance in the reconstruction over the ensemble of datasets. However, a disadvantage with this technique is that repeated scanning is needed for reconstruction of multiple images, thereby making it computationally inefficient and impractical in application. A computationally efficient method for determining voxel variance data and generating variance images is therefore desired. It would also be useful to develop ways to use and apply such information, such as in analysis of reconstructed CT images, or for improved image acquisition or reconstruction.
In attempting to detect contraband in enclosed containers, such as, for example, luggage or parcels, one difficulty in reconstructing images of the contraband is being able to properly segment the reconstructed images, namely being able to distinguish the contraband from any other object located in the enclosed container. A computationally efficient method for segmenting one object image from another is therefore desired.